Netflix Settles Privacy Lawsuit, Cancels Prize Sequel

On Friday, Netflix announced on its corporate blog that it has settled a lawsuit related to its Netflix Prize, a $1 million contest that challenged machine learning experts to use Netflix's data to produce better recommendations than the movie giant could serve up themselves.

The lawsuit called attention to academic research that suggests that Netflix indirectly exposed the movie preferences of its users by publishing anonymized customer data. In the suit, plaintiff Paul Navarro and others sought an injunction preventing Netflix from going through the so-called "Netflix Prize II," a follow-up challenge that Netflix promised would offer up even more personal data such as genders and zipcodes.

"Netflix is not going to pursue a sequel to the Netflix Prize," says spokesman Steve Swasey. "We looked at this, we heard some dissension and so we've settled it, resolved the issues and are moving on."

Netflix's decision to forestall the so-called "Netflix Prize II" was part of the settlement agreement, says Scott Kamber, the plaintiff's attorney. Also part of the settlement and per industry norms, Netflix is not admitting any wrongdoing.

According to the company's blog post, Netflix has also settled an until now-unknown negotiation with the Federal Trade Commission.

The financial terms--if any--remain undisclosed, but Kamber has been involved in multi-million dollar privacy suits before, including the $10 million Facebook Beacon settlement.

The movie rental company captured the world's attention when it announced the Netflix Prize in October 2006, offering a reward to anyone who could improve upon Netflix's personalized movie recommendations by a 10% margin. That mandate wasn't achieved for close to three years and, at one point, was generally thought to be impossible.

Several competing teams ended up joining forces and the contest quickly became so close that nobody knew which of two amalgamated teams would end up on top. It was a mashup of three competing teams, dubbed "BellKor's Pragmatic Chaos," that ended up taking home the check from Netflix chief executive Reed Hastings last September.

The Netflix Prize's great draw--and ultimate flaw-- was capturing the world's imagination and some of its greatest minds. Like the folkloric John Henry and the steam engine, the contest drew machine learning experts from leading academic and private research firms and the attention of publications like The New York Times and Forbes.

James Bennett, then vice president of recommendations at Netflix, issued the initial proclamation at a highly specialized industry conference in Spain. In the end, 50,000 contestants wound up participating.

"We thought that it would generated some big interest among a couple hundred machine learning experts," says Swasey. "It became a news-making machine."

The surprise popularity of the contest, however, came with the unintended consequence of also gaining attention from privacy researchers like Arvind Narayanan and Vitaly Shmatikov, who showed that a second set of information such as comments on the popular Internet Movie Database could help third parties triangulate the identity of the "anonymous" Netflix customers.

Kamber, who brought the suit, says that it was in part the massive attention to the Netflix prize that brought clients to him and compelled some of them to seek an injunction against the announced follow-up. He argues that his clients don't want to prevent the kind of technological and academic innovation that Netflix was trying to encourage, but rather just want to make sure Netflix's customers' privacy is protected.

"My clients specifically expressed an interest in making sure that those opportunities were not foreclosed," says Kamber. "The resolution allows Netfix to potentially tap into that community in the future, but do so in a way that makes customers confident that their information is going to be safeguard."

Interestingly, privacy expert Larry Ponemon says that Netflix could have likely avoided the matter altogether by using a technique called "data masking" that would have randomized its data set while still keeping the data relevant to developers.

A spokesperson for Dataguise, a data masking company, says that it would have cost the company around $50,000 for a license.